Structural damage identification utilising PCA-compressed frequency response functions and neural network ensembles

Dackermann, U; Li, J; Samali, B

Structural damage identification utilising PCA-compressed frequency response functions and neural network ensembles

Dackermann, U Li, J

Samali, B

Permalink

Publication Type:: Conference Proceeding
Citation:: Futures in Mechanics of Structures and Materials - Proceedings of the 20th Australasian Conference on the Mechanics of Structures and Materials, ACMSM20, 2008, pp. 803 - 809
Issue Date:: 2008-12-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (425.51 kB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Dackermann, U	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048	en_US
dc.contributor.author	Samali, B https://orcid.org/0000-0002-3426-7745	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Futures in Mechanics of Structures and Materials - Proceedings of the 20th Australasian Conference on the Mechanics of Structures and Materials, ACMSM20, 2008, pp. 803 - 809	en_US
dc.identifier.isbn	9780415491969	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11056
dc.description.abstract	This paper presents a damage detection method that utilises FRF data to identify damage in beam structures. The proposed method uses artificial neural networks (ANNs) to map changes in FRFs to damage characteristics. To obtain suitable patterns for ANN inputs, the size of the FRFs is reduced adopting Principal Component Analysis (PCA) techniques. A hierarchy of neural network ensembles is created to take advantage of individual differences from sensor signals. To simulate field applications, the time history data are polluted with white Gaussian noise. The method involves finite element modelling of undamaged and damaged steel beams. By performing transient analysis with the numerical beams, the time histories are obtained and subsequently polluted with different levels of white Gaussian noise. FRFs are determined and compressed utilising PCA techniques. The PCA-reduced FRFs are then used as input patterns for training and testing of neural network ensembles giving the characteristics of the damage. © 2009 Taylor & Francis Group, London.	en_US
dc.relation.ispartof	Futures in Mechanics of Structures and Materials - Proceedings of the 20th Australasian Conference on the Mechanics of Structures and Materials, ACMSM20	en_US
dc.title	Structural damage identification utilising PCA-compressed frequency response functions and neural network ensembles	en_US
dc.type	Conference Proceeding
utslib.for	090505 Infrastructure Engineering and Asset Management	en_US
utslib.for	090506 Structural Engineering	en_US
dc.location.activity	Toowoomba	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents a damage detection method that utilises FRF data to identify damage in beam structures. The proposed method uses artificial neural networks (ANNs) to map changes in FRFs to damage characteristics. To obtain suitable patterns for ANN inputs, the size of the FRFs is reduced adopting Principal Component Analysis (PCA) techniques. A hierarchy of neural network ensembles is created to take advantage of individual differences from sensor signals. To simulate field applications, the time history data are polluted with white Gaussian noise. The method involves finite element modelling of undamaged and damaged steel beams. By performing transient analysis with the numerical beams, the time histories are obtained and subsequently polluted with different levels of white Gaussian noise. FRFs are determined and compressed utilising PCA techniques. The PCA-reduced FRFs are then used as input patterns for training and testing of neural network ensembles giving the characteristics of the damage. © 2009 Taylor & Francis Group, London.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/11056